Biology subjects

David, E.

Publications and source records attributed to David, E..

At least 19 recordsLinked to original sources

bioRxiv2026

Synovium-Restricted Armored PD-1-Targeted CAR-T Cells Reprogram Immunity and Resolve Experimental Arthritis

Despite major therapeutic advances, a substantial fraction of patients with autoimmune disease remains refractory to treatment. While B cell-targeted CAR-T therapies have shown considerable efficacy, the central contribution of pathogenic T cells to rheumatoid arthritis (RA) suggests that complementary T cell-directed strategies may enable deeper disease control. Using single-cell multi-omics of human RA and experimental models, PDCD1 was identified as a selective marker of synovial disease-associated T cells. We developed PD-1-directed CAR-T cells that potently eliminate these cells in vitro and in vivo, leading to marked attenuation of synovitis in RA models. To limit off-target activity, we engineered NR4A2-driven CAR-responsive biosensors to restrict CAR activity to inflamed synovium. To couple anti-PD-1 CAR-mediated cytotoxicity with microenvironmental modulation, we further engineered these CAR-T cells to secrete soluble TNF receptor II (sTNFRii), counteracting baseline inflammation and CAR-induced IFN response and promoting a tissue-reparative myeloid state. PD-1-targeted CAR-T therapy thus represents a promising, specific, and safe strategy for autoimmune diseases involving disease-associated T cells.

immunology↗

bioRxiv2026

A framework for reparative CAR T engineering in the CNS

Chimeric antigen receptor (CAR) T cells have shown remarkable therapeutic promise in hematological malignancies and, more recently, in autoimmunity. They also hold considerable potential for neurodegenerative diseases and CNS injury, where the therapeutic objective shifts from cell depletion to immune modulation and tissue repair. Using ischemic stroke as proof-of-concept, we engineered MOG-targeting CAR T cells to dissect how distinct CAR designs shape the CNS microenvironment. CD4/CD8 CAR T cells (a mixture of CD4 and CD8 subsets) proliferated robustly and efficiently infiltrated the ischemic hemisphere, but induced broad immune recruitment and exacerbated neuroinflammation. In contrast, CD4 restricted CAR T cells markedly reduced immune infiltration, reprogrammed microglia, and minimized inflammatory activation. We engineered CD4 CAR T cells to secrete brain-derived neurotrophic factor (BDNF) to determine whether they could be redirected toward a reparative, non-cytotoxic phenotype. CD4 BDNF-CAR T cells further attenuated inflammation, reduced immune infiltration, and promoted the expansion of regulatory T cells. CD4 BDNF-CAR T treated mice showed significantly improved gait performance following stroke. Together, these findings establish a cellular framework and outline principles for engineering reparative CAR T platforms for neurological diseases.

immunology↗

bioRxiv2025

The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset

Computer vision is increasingly used in farmers fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimetre ground sampling distance enable the detection and tracking of plant features, including size, shape, and colour. Although todays AI-driven foundation models segment almost any object in an image, they still fail for complex plant canopies. To improve model performance, the global wheat dataset consortium assembled a diverse set of images from experiments around the globe. After the head detection dataset (GWHD), the new dataset targets a full semantic segmentation (GWFSS) of wheat organs (leaves, stems and spikes) covering all developmental stages. Images were collected by 11 institutions using a wide range of imaging setups. Two datasets are provided: i) a set of 1096 diverse images in which all organs were labelled at the pixel level, and (ii) a dataset of 52,078 images without annotations available for additional training. The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer. Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca. 90%. However, the precision for stems with 54% was rather lower. The major advantages over published models are: i) the exclusion of weeds from the wheat canopy, ii) the detection of all wheat features including necrotic and senescent tissues and its separation from crop residues. This facilitates further development in classifying healthy vs. unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.

plant biology↗

bioRxiv2025

TMPRSS2-ERG confers resistance to antiandrogens: mechanism and therapeutic implications

Approximately 50% of prostate cancer (PCa) patients harbor fusions involving the TMPRSS2 and ERG genes. Despite this, tailored therapies targeting the fused gene, tERG, remain undeveloped. Our study analyzed biopsy samples from two clinical trials assessing the efficacies of androgen receptor (AR) signaling inhibitors (ARSIs). The results revealed that tERG promotes resistance to ARSIs and is associated with elevated levels of the glucocorticoid receptor (GR). Subsequent assays showed that GR directly interacts with tERG, alleviates allosteric autoinhibition and prevents chemotherapy-induced tERG degradation. In PCa models, either inhibiting GR or lowering cortisol levels suppressed tumor growth in tERG-positive models, but not in fusion-negative models. In addition, patient-derived fusion-positive xenografts displayed enhanced sensitivity to combined GR and AR inhibitors. Collectively, these findings highlight TMPRSS2-ERG as a new biomarker and propose that simultaneous inhibition of GR and AR may specifically benefit tERG-positine patients. However, GR stimulatory corticosteroid therapies may not be advisable for this patient subgroup.

cancer biology↗

bioRxiv2025

T cell-derived IFN-γ Suppresses T Follicular Helper Cell Differentiation and Antibody Responses during Viral Infections

CD4+ T cells play a critical role in antiviral humoral and cellular immune responses. We have previously reported that subcutaneous lymphocytic choriomeningitis virus (s.c. LCMV) infection is characterized by a stark compartmentalization of CD4+ T cells, leading to strong TH1 polarization but virtually absent T follicular helper (TFH) cells, a key driver of humoral immunity. Here, we investigated the mechanisms responsible for this impaired TFH differentiation. We found that T-bet+ cells induced by s.c. LCMV infection encompass a TH1 subset expressing Granzyme-B (GzmB) and a Tcf-1+ subset that retains the potential for TFH differentiation without expressing mature TFH markers. Interestingly, IFN-{gamma} blockade enables full differentiation of Tcf-1+ cells into TFH, formation of germinal centers and increased antibody production. Of note, the suppression of TFH cells by IFN-{gamma} is not directly mediated through CD4+ T cells but rather involves another cell type, likely dendritic cells (DCs). Our study provides novel insights into the mechanisms directing early CD4+ T cell polarization and affecting humoral responses to viruses, laying a foundation for the development of effective vaccine strategies.

immunology↗

bioRxiv2024

Systematic functional assessment of antiphage systems in their native host

Bacterial resistance to bacteriophages (phages) relies on two primary strategies: preventing phage attachment and blocking post-attachment steps. These post-attachment mechanisms are mediated by diverse defence systems, including DNA-degrading systems such as Restriction-Modification (RM) and CRISPR-Cas, as along with abortive infection systems that induce cell death or dormancy. Computational analyses suggest that bacterial genomes encode multiple defence systems, which may act synergistically to enhance phage resistance. However, the regulation, interactions, and ecological roles of these systems in native hosts remain poorly understood. This study explored the role of eight predicted defence systems in the clinical isolate NILS69 of E. coli by testing its susceptibility to 93 phages. Infectivity and adsorption assays using mutants defective in these systems revealed that only PD-T4-3 and RM systems restricted phages able to adsorb. The RM system acted via a predicted Type IV endonuclease and was also able to limit plasmid conjugation if the plasmid was transferred from a donor strain lacking a methylase, which is the hallmark of Type I, II or III RM systems. Other defence systems showed no detectable activity, likely due to phage specificity, environmental regulation, or cofactor requirements. These findings underscore the need for further studies to investigate the regulation and ecological roles of bacterial defence systems in their native host contexts.

microbiology↗

bioRxiv2024

Natural killer cell function is regulated by TGF-β signaling in pregnancy and tumor progression

The immune compartment of the maternal-fetal interface must balance between supporting maternal-fetal interactions and maintaining maternal tolerance. Despite recent advances, the cellular and molecular regulators that drive maternal immune cell remodeling remain largely unknown. Using index and transcriptional single-cell sorting, we comprehensively characterized the immune compartment dynamics in the maternal-fetal interface of both human and mouse and charted the markers and functional pathways associated with these cells. Comparing immune signatures in decidua and tumors of human and mice, we identify conserved gene modules that are activated in natural killer (NK) cells of both compartments, including TFG-{beta} signaling. Genetic ablation and antibody blockade of the TGF-{beta} pathway in NK cells resulted in enhanced anti-tumor immunity.

immunology↗

bioRxiv2024

Single-cell variations of circadian clock and immune gene expression in microglia and neurodegeneration

We investigated the diurnal rhythmicity of gene expression in microglia, the resident macrophages of the brain, in health and disease. Using RNA sequencing and single-cell analysis by RNAscope, we examined wild-type mice and the R6/2 transgenic mouse model of Huntingtons disease (HD). Our findings suggest context-dependent rhythmic gene expression in microglia, exhibiting substantial variability between individual cells and brain regions over 24 hours. Notably, we observed loss of rhythmic gene expression of key clock genes in microglia from symptomatic but not presymptomatic R6/2 mice. Moreover, we identified de novo 24-hour rhythmic gene expression and altered diurnal patterns of immune-related genes associated with neurodegenerative diseases in microglia from symptomatic R6/2 mice. Our findings suggest circadian reprogramming of microglia in the context of neurodegeneration.

neuroscience↗

bioRxiv2024

Ephrin-B2 promotes nociceptive plasticity and hyperalgesic priming through EphB2-MNK-eIF4E signaling in both mice and humans

Ephrin-B-EphB signaling promotes pain through signaling between dorsal root ganglion (DRG) neurons and spinal cord neurons in the dorsal horn, and through signaling between peripheral cells and EphB receptors expressed by DRG neurons. Previous findings link ephrin-B expression in painful peripheral tissues in patients to chronic pain, suggesting the clinical significance of this signaling, but the direct effects of ephrins on DRG neurons have not been widely studied. We hypothesized that ephrin-B2 would promote nociceptor plasticity and hyperalgesic priming through MNK-eIF4E signaling, a critical mechanism for nociceptive plasticity induced by growth factors, cytokines and nerve injury. Our work demonstrates that ephrin-B2-EphB2 signaling drives activation of MNK-eIF4E in DRG neurons to cause an enhanced response to inflammatory mediator signaling in both mice and humans and hyperalgesic priming in two models in mice. Both male and female mice developed dose-dependent mechanical hypersensitivity in response to ephrin-B2, and both sexes showed hyperalgesic priming when challenged with PGE2 injection into the same hindpaw. Acute nociceptive behaviors and hyperalgesic priming were blocked in mice lacking MNK1 (Mknk1 knockout mice) and by the MNK inhibitor eFT508. Similar effects on hyperalgesic priming were seen in a dural injection model. We generated a sensory neuron specific knockout of EphB2 using Pirt-Cre mice and found that these mice lacked responses to ephrin-B2 injection. We used Ca2+-imaging to determine direct effects of ephrin-B2 on DRG neurons and found that ephrin-B2 treatment enhanced Ca2+ transients in response to PGE2 which were absent in DRG neurons from MNK1-/- and EphB2-PirtCre mice. In experiments on human DRG neurons we found that ephrin-B2 increased eIF4E phosphorylation and enhanced Ca2+ responses to PGE2 treatment, both of which were blocked by eFT508 treatment. We conclude that ephrin-B2 acts directly on mouse and human sensory neurons to induce nociceptor plasticity via MNK-eIF4E signaling. The findings offer insight into how ephrin-B signaling promotes pain, and suggests treatment avenues for prevention or reversal of chronic pain associated with EphB activation in sensory neurons. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=153 HEIGHT=200 SRC="FIGDIR/small/581414v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@9ff5c9org.highwire.dtl.DTLVardef@1c17916org.highwire.dtl.DTLVardef@f4a5b0org.highwire.dtl.DTLVardef@1aa00c6_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2022

Single-cell atlas of mouse limb development reveals a complex spatiotemporal dynamics of skeleton formation

Limb development has long served as a model system for coordinated spatial patterning of progenitor cells. Here, we identify a population of naive limb progenitors and show that they differentiate progressively to form the skeleton in a complex nonconsecutive three-dimensional pattern. Single-cell RNA sequencing of the developing mouse forelimb revealed three progenitor states: naive, proximal and autopodial, as well as Msx1 as a marker for the naive progenitors. In vivo lineage tracing confirmed this role and localized the naive progenitors to the outer margin of the limb, along the anterior-posterior axis. Sequential pulse-chase experiments showed that the progressive transition of Msx1+ naive progenitors into proximal and autopodial progenitors coincides with their differentiation to Sox9+ chondroprogenitors, which occurs along all the forming skeletal segments. Indeed, tracking the spatiotemporal sequence of differentiation showed that the skeleton forms progressively in a complex pattern. These findings suggest a new model for limb skeleton development.

developmental biology↗

bioRxiv2022

SegVeg: Segmenting RGB images into green and senescent vegetation by combining deep and shallow methods

The pixels segmentation of high resolution RGB images into background, green vegetation and senescent vegetation classes is a first step often required before estimating key traits of interest including the vegetation fraction, the green area index, or to characterize the sanitary state of the crop. We developed the SegVeg model for semantic segmentation of RGB images into the three classes of interest. It is based on a U-net model that separates the vegetation from the background. It was trained over a very large and diverse dataset. The vegetation pixels are then classified using a SVM shallow machine learning technique trained over pixels extracted from grids applied to images. The performances of the SegVeg model are then compared to a three classes U-net model trained using weak supervision over RGB images with predicted pixels by SegVeg as groundtruth masks. Results show that the SegVeg model allows to segment accurately the three classes, with however some confusion mainly between the background and the senescent vegetation, particularly over the dark and bright parts of the images. The U-net model achieves similar performances, with some slight degradation observed for the green vegetation: the SVM pixel-based approach provides more precise delineation of the green and senescent patches as compared to the convolutional nature of U-net. The use of the components of several color spaces allows to better classify the vegetation pixels into green and senescent ones. Finally, the models are used to predict the fraction of the three classes over the grids pixels or the whole images. Results show that the green fraction is very well estimated (R2=0.94) by the SegVeg model, while the senescent and background fractions show slightly degraded performances (R2=0.70 and 0.73, respectively). We made SegVeg publicly available as a ready-to-use script, as well as the entire dataset, rendering segmentation accessible to a broad audience by requiring neither manual annotation nor knowledge, or at least, offering a pre-trained model to more specific use.

plant biology↗

bioRxiv2021

Neighbor-specific gene expression revealed from physically interacting cells during mouse embryonic development

Development of multicellular organisms is orchestrated by persistent cell-cell communication between neighboring partners. Direct interaction between different cell types can induce molecular signals that dictate lineage specification and cell fate decisions. Current single cell RNAseq (scRNAseq) technology cannot adequately analyze cell-cell contact-dependent gene expression, mainly due to the loss of spatial information. To overcome this obstacle and resolve cell-cell contact-specific gene expression during embryogenesis, we performed RNA sequencing of physically interacting cells (PIC-seq) and assessed them alongside similar single cell transcriptomes derived from developing mouse embryos between embryonic day (E) 7.5 and E9.5. Analysis of the PIC-seq data identified novel gene expression signatures that were dependent on the presence of specific neighboring cell types. Our computational predictions, validated experimentally, demonstrated that neural progenitor (NP) cells overexpress Lhx5 and Nkx2-1 genes, when exclusively interacting with the definitive endoderm (DE) cell. Moreover, there was a reciprocal impact on the transcriptome of the DE cells, as they tend to overexpress Rax and Gsc genes when in contact with the NP cells. Using individual cell transcriptome data, we formulated a means of computationally predicting the impact of one cell type on the transcriptome of its neighboring cell types. We have further developed a distinctive spatial-tSNE to display the pseudo-spatial distribution of cells in a 2-dimensional space. In summary, we describe an innovative approach to study contact-specific gene regulation during embryogenesis with potential broader implication in other physiologically relevant processes. SignificancePhysical contact between neighboring cells is known to induce transcriptional changes in the interacting partners. Accurate measurement of these cell-cell contact based influences on the transcriptome is a very difficult experimental task. However, determining such transcriptional changes will highly enhance our understanding for the developmental processes. Current scRNAseq technology isolates the tissue into individual cells, making it hard to determine the potential transcriptomic changes due to its interacting partners. Here, we combined PIC-seq and computational algorithms to identify cell-type contact dependent transcriptional profiles focusing on endoderm development. We have computationally identified and experimentally validated specific gene expression patterns depending upon the presence of specific neighboring cell types. Our study suggests a new way to study cell-cell interactions for embryogenesis.

developmental biology↗

bioRxiv2021

Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation

The function of mammalian cells is largely influenced by their tissue microenvironment. Advances in spatial transcriptomics open the way for studying these important determinants of cellular function by enabling a transcriptome-wide evaluation of gene expression in situ. A critical limitation of the current technologies, however, is that their resolution is limited to niches (spots) of sizes well beyond that of a single cell, thus providing measurements for cell aggregates which may mask critical interactions between neighboring cells of different types. While joint analysis with single-cell RNA-sequencing (scRNA-seq) can be leveraged to alleviate this problem, current analyses are limited to a discrete view of cell type proportion inside every spot. This limitation becomes critical in the common case where, even within a cell type, there is a continuum of cell states that cannot be clearly demarcated but reflects important differences in the way cells function and interact with their surroundings. To address this, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI), a probabilistic method for multi-resolution analysis for spatial transcriptomics that explicitly models continuous variation within cell types. Using simulations, we demonstrate that DestVI is capable of providing higher resolution compared to the existing methods and that it can estimate gene expression by every cell type inside every spot. We then introduce an automated pipeline that uses DestVI for analysis of single tissue slices and comparison between tissues. We apply this pipeline to study the immune crosstalk within lymph nodes to infection and explore the spatial organization of a mouse tumor model. In both cases, we demonstrate that DestVI can provide a high resolution and accurate spatial characterization of the cellular organization of these tissues, and that it is capable of identifying important cell-type-specific changes in gene expression - between different tissue regions or between conditions. DestVI is available as an open-source software package in the scvi-tools codebase (https://scvi-tools.org).

bioinformatics↗

bioRxiv2021

Plant detection and counting from high-resolution RGB images acquired from UAVs: comparison between deep-learning and handcrafted methods with application to maize, sugar beet, and sunflower crops

Progresses in agronomy rely on accurate measurement of the experimentations conducted to improve the yield component. Measurement of the plant density is required for a number of applications since it drives part of the crop fate. The standard manual measurements in the field could be efficiently replaced by high-throughput techniques based on high-spatial resolution images taken from UAVs. This study compares several automated detection of individual plants in the images from which the plant density can be estimated. It is based on a large dataset of high resolution Red/Green/Blue (RGB) images acquired from Unmanned Aerial Vehicules (UAVs) during several years and experiments over maize, sugar beet and sunflower crops at early stages. A total of 16247 plants have been labelled interactively on the images. Performances of handcrafted method (HC) were compared to those of deep learning (DL). The HC method consists in segmenting the image into green and background pixels, identifying rows, then objects corresponding to plants thanks to knowledge of the sowing pattern as prior information. The DL method is based on the Faster Region with Convolutional Neural Network (Faster RCNN) model trained over 2/3 of the images selected to represent a good balance between plant development stage and sessions. One model is trained for each crop. Results show that simple DL methods generally outperforms simple HC, particularly for maize and sunflower crops. A significant level of variability of plant detection performances is observed between the several experiments. This was explained by the variability of image acquisition conditions including illumination, plant development stage, background complexity and weed infestation. The image quality determines part of the performances for HC methods which makes the segmentation step more difficult. Performances of DL methods are limited mainly by the presence of weeds. A hybrid method (HY) was proposed to eliminate weeds between the rows using the rules developed for the HC method. HY improves slightly DL performances in the case of high weed infestation. When few images corresponding to the conditions of the testing dataset were complementing the training dataset for DL, a drastic increase of performances for all the crops is observed, with relative RMSE below 5% for the estimation of the plant density.

plant biology↗

bioRxiv2021

Physically interacting beta-delta pairs in the regenerating pancreas revealed by single cell sequencing

The endocrine pancreas is able to regenerate in response to insult, including by driving beta-cells into the cell division cycle. Until recently, communication between neighboring cells in islets of Langerhans was overlooked by single-cell genomic technologies, which require rigorous tissue dissociation into single cells. Here, we utilize sorting of physically interacting cells (PICs) with single-cell RNA-sequencing to systematically map cellular interactions in the regenerating endocrine pancreas. The cellular landscape of the regenerated pancreas features regeneration-associated endocrine populations. We explore the unexpected heterogeneity of beta-cells in regeneration, including an interaction-specific program between paired beta and delta-cells. Our analysis suggests that the particular cluster of beta-cells that pair with delta-cells benefits from stress protection, implying that the interaction between beta and delta-cells safeguards against regeneration-associated challenges.

cell biology↗

bioRxiv2020

Single Cell Analysis of Regions of Interest (SCARI) using a novel photoswitchable tag

The functional activity and differentiation potential of cells is determined by their interaction with surrounding cells. Approaches that allow the unbiased characterization of cell states while at the same time providing spatial information are of major value to assess this environmental influence. However, most current techniques are hampered by a trade-off between spatial resolution and cell profiling depth. Here, we developed a photoswitch-based technology that allows the isolation and in-depth analysis of live cells from regions of interest in complex ex vivo systems, including human tissues. The use of a highly sensitive 4-nitrophenyl(benzofuran)-cage coupled to nanobodies allowed photoswitching of cells in areas of interest with low-intensity violet light and without detectable phototoxicity. Single cell RNA sequencing of spatially defined CD8+ T cells was used to exemplify the feasibility of identifying location-dependent cell states at the single cell level. Finally, we demonstrate the efficient labeling and photoswitching of cells in live primary human tumor tissue. The technology described here provides a valuable tool for the analysis of spatially defined cells in diverse biological systems, including clinical samples.

bioengineering↗

bioRxiv2020

Cancer-associated fibroblast compositions change with breast cancer progression linking S100A4 and PDPN ratios with clinical outcome

Tumors are supported by cancer-associated fibroblasts (CAFs). CAFs are heterogeneous and carry out distinct cancer-associated functions. Understanding the full repertoire of CAFs and their dynamic changes could improve the precision of cancer treatment. CAFs are usually analyzed at a single time-point using specific markers, and it is therefore unclear whether CAFs display plasticity as tumors evolve. Here, we analyze thousands of CAFs using index and transcriptional single-cell sorting, at several time-points along breast tumor progression in mice, uncovering distinct subpopulations. Strikingly, the transcriptional programs of these subpopulations change over time and in metastases, transitioning from an immune-regulatory program to wound healing and antigen-presentation programs, indicating that CAFs and their functions are dynamic. Two main CAF subpopulations are also found in human breast tumors, where their ratio is associated with disease outcome across subtypes, and is particularly correlated with BRCA mutations in triple-negative breast cancer. These findings indicate that the repertoire of CAFs changes over time in breast cancer progression, with direct clinical implications.

cancer biology↗

bioRxiv2019

Transneuronal interactions facilitate axonal compartment formation

The mechanisms controlling wiring of neuronal networks are largely unknown. The stereotypic architecture of the Drosophila mushroom-body (MB) offers a unique system to study circuit assembly. The adult medial MB {gamma}-lobe is comprised of a long bundle of axons that wires with specific modulatory and output neurons in a tiled manner defining five distinct zones. We found that the immunoglobulin superfamily protein Dpr12 is cell-autonomously required in {gamma}-neurons for their developmental regrowth into the distal {gamma}4/5 zones, where both Dpr12 and its interacting protein, DIP-{delta}, are enriched. DIP-{delta} functions in a subset of dopaminergic neurons that wire with {gamma}-neurons within the {gamma}4/5 zone. During metamorphosis, these dopaminergic projections arrive to the {gamma}4/5 zone prior to {gamma}-axons, suggesting that {gamma}-axons extend through a prepatterned region. Thus, Dpr12/DIP-{delta} transneuronal interaction is required for {gamma}4/5 zone formation. Our study sheds light onto molecular and cellular mechanisms underlying circuit formation within subcellular resolution.

neuroscience↗